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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Inovasi Teknologi Pendidikan Register: Jurnal Ilmiah Teknologi Sistem Informasi Briliant: Jurnal Riset dan Konseptual Sinkron : Jurnal dan Penelitian Teknik Informatika Syntax Literate: Jurnal Ilmiah Indonesia JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL EDUCATION AND DEVELOPMENT MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Entrepreneur dan Entrepreneurship (JEE) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer bit-Tech Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pendidikan dan Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Evan Tanuwijaya
Universitas Ciputra Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Modifikasi Arsitektur VGG16 untuk Klasifikasi Citra Digital Rempah-Rempah Indonesia Evan Tanuwijaya; Angelica Roseanne
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1492

Abstract

Rempah-rempah merupakan salah satu kekayaan alam yang dimiliki oleh Indonesia. Rempah-rempah sendiri memiliki banyak manfaat untuk Kesehatan ataupun hal-hal lain. Dari banyaknya rempah yang berada di Indonesia, ternyata masyarakat Indonesia sendiri masih memiliki pengetahuan yang rendah akan rempah-rembah tersebut. Hal ini menyebabkan banyak orang bahkan petani mengalami kesusahan dalam mengenali jenis rempah terutama remaja. Membedakan rempah satu dengan yang lain merupakan tantangan yang banyak dihadapi oleh masyarakat. Oleh sebab itu, penelitian ini membuat sebuah model klasifikasi dengan menggunakan convolution neural network dengan arsitektur VGG 16 yang dimodifikasi. Arsitektur modifikasi VGG 16 memiliki 10-layer yang terdiri dari 7-layer convolution dan 3-layer fully connected. Untuk fase latih model modifikasi VGG 16 ini menggunakan dataset rempah yang disediakan oleh Kaggle. Validasi model yang digunakan adalah akurasi, loss, precision, dan recall untuk membandingkan model mana yang memiliki nilai yang terbaik. Untuk model modifikasi VGG 16 yang dibuat untuk melakukan klasifikasi, mendapatkan hasil evaluasi rata-rata akurasi sebesar 81%, nilai recall sebesar 76%, dan nilai precision sebesar 81% untuk fase training dan untuk fase validasi, akurasi sebesar 85%, nilai recall sebesar 80%, dan nilai precision sebesar 84%. Jadi dengan model modifikasi VGG 16 dapat disimpulkan bahwa model mampu memprediksi rempah-rempah lebih baik dari model Alexnet.
Perbandingan Kinerja Model CNN untuk Klasifikasi Kematangan Pisang pada Android Low-End Orlando, Owen; Tanuwijaya, Evan
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3442

Abstract

The ripeness level of bananas is an important indicator that determines the quality, selling value, and suitability of distribution in the agricultural supply chain. However, manual maturity assessments are still subjective and difficult to apply consistently on a large scale. The use of Convolutional Neural Networks (CNN) offers a more accurate and objective solution, but most previous studies have only evaluated high-powered devices so they do not reflect the real performance of low-spec smartphones. This study aims to compare the efficiency of three lightweight CNN architectures: MobileNetV1, EfficientNetB0, and NASNetMobile for the classification of banana ripeness and evaluate its feasibility of being implemented on low-end Android devices. The research method included model training using the Banana Ripeness Classification dataset containing 13,478 images with three maturity classes. Augmentation-based oversampling was applied to address data imbalances, while all three models were trained on transfer learning strategies before being converted to the TensorFlow Lite format. Direct testing was conducted on the Samsung Galaxy A3 (2016) device to measure accuracy, inference time, model size, and RAM usage. The experimental results showed that MobileNetV1 provided the best performance with an accuracy of 98.14%, an inference time of 287.57 ms, and a model size of 3.23 MB, much more efficient than EfficientNetB0 and NASNetMobile. In conclusion, MobileNetV1 is the most optimal architecture for Android-based banana ripeness classification applications on low-spec devices, while making an empirical contribution to the selection of efficient CNN models for mobile implementation in the context of digital agriculture.
Evaluation of User Interface (UI) and User Experience (UX) in A Web-Based Entrepreneurial Student Application Ellya Kurniawan, Jimmy; Rahmawati, Kuncoro Dewi; Larasati Rembulan, Cicilia; Tanuwijaya, Evan
Jurnal Entrepreneur dan Entrepreneurship Vol. 15 No. 1 (2026): Jurnal Entrepreneur dan Entrepreneurship
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/jee.v15i1.6402

Abstract

Measurement of entrepreneurial orientation remains limited and is predominantly conducted using manual methods. To address this gap, this study aims to develop a web-based application for measuring entrepreneurial orientation—which has not previously been developed—and to examine its reliability, user interface (UI), and user experience (UX). The instrument used is an adaptation of the entrepreneurial orientation scale, which was converted into a website-based format and complemented with recommendation features and user constraint identification. Data were collected through a focus group discussion (FGD) involving junior and senior high school teachers and students (N = 5), as well as a survey of junior and senior high school students (N = 60) following a trial of the web-based "Entrepreneurial Student" application. The results indicate that this web-based instrument demonstrates high reliability, with Cronbach’s alpha ranging from 0.806 to 0.845, and adequate validity, with Corrected Item–Total Correlation (CITC) values ranging from 0.404 to 0.630. After revisions based on user feedback, UI scores were in the "Very Good" category (range 495–554), and UX scores were also in the "Very Good" category (range 258–274). The Entrepreneurial Student application demonstrates promising potential for measuring entrepreneurial orientation among secondary school students. With further testing involving larger samples, the application may be implemented more broadly within the adolescent population.
Co-Authors AA Sudharmawan, AA Agus Zainal Arifin Angelica Roseanne Arianindita, Achmad Rijalu Arif Mudi Priyatno Baskoro Nugroho Chastine Fatichah Christian Tanjono David Christian Kartamihardja Derend Marvel Hanson Prionggo Dini Adni Navastara, Dini Adni Dylan Lienardi , Nicholas Eha Renwi Astuti Ellya Kurniawan, Jimmy Jimmy Ellya Kurniawan Kenneth Sutjiawan Kristanto, Samuel Miracle Kristanto, Timothy Kuncoro Dewi Rahmawati Larasati Rembulan, Cicilia Laura Mahendratta Tjahjono Lestari, Caecilia Citra Mohammad Fatoni Anggris, Mohammad Fatoni Mychael Maoeretz Engel Naya, Rafi Abhista Noviando Yuliando Orlando, Owen Rarasmaya Indraswari Reinaldo Lewis Lordianto Reiner Anggriawan Jasin Rinabi Tanamal Rudy Susanto Rudy Susanto Safri Adam Setiawan, Averill Saladin Atma Siswanto, Felicia Devina Sutanto, Richard Evan Theresia Ratih Dewi Saputri Timotius Timotius Leonardo Lianoto Tota Pirdo Kasih Tota Pirdo Kasih Yaurentius, Evelyn Callista Yuwono Marta Dinata